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| http://dx.doi.org/10.1002/anie.201007685 | DOI Listing |
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Missing value replacement in strings and applications.
Data Min Knowl Discov
January 2025
CWI, Amsterdam, The Netherlands.
Missing values arise routinely in real-world sequential (string) datasets due to: (1) imprecise data measurements; (2) flexible sequence modeling, such as binding profiles of molecular sequences; or (3) the existence of confidential information in a dataset which has been deleted deliberately for privacy protection. In order to analyze such datasets, it is often important to replace each missing value, with one or more letters, in an efficient and effective way. Here we formalize this task as a combinatorial optimization problem: the set of constraints includes the of the missing value (i.
View Article and Find Full Text PDFAn easy-to-use three-dimensional protein-structure-prediction online platform "DPL3D" based on deep learning algorithms.
Curr Res Struct Biol
June 2025
The College of Health Humanities, Jinzhou Medical University, Jinzhou, 121001, China.
The change in the three-dimensional (3D) structure of a protein can affect its own function or interaction with other protein(s), which may lead to disease(s). Gene mutations, especially missense mutations, are the main cause of changes in protein structure. Due to the lack of protein crystal structure data, about three-quarters of human mutant proteins cannot be predicted or accurately predicted, and the pathogenicity of missense mutations can only be indirectly evaluated by evolutionary conservation.
View Article and Find Full Text PDFFully automated in vivo screening system for multi-organ imaging and pharmaceutical evaluation.
Microsyst Nanoeng
January 2025
Guangdong Provincial Key Laboratory of Sensor Technology and Biomedical Instrument, School of Biomedical Engineering, Shenzhen Campus of Sun Yat-Sen University, 518000, Shenzhen, China.
Advancements in screening technologies employing small organisms have enabled deep profiling of compounds in vivo. However, current strategies for phenotyping of behaving animals, such as zebrafish, typically involve tedious manipulations. Here, we develop and validate a fully automated in vivo screening system (AISS) that integrates microfluidic technology and computer-vision-based control methods to enable rapid evaluation of biological responses of non-anesthetized zebrafish to molecular gradients.
View Article and Find Full Text PDFAutomated AI-based image analysis for quantification and prediction of interstitial lung disease in systemic sclerosis patients.
Respir Res
January 2025
National Heart and Lung Institute, Imperial College London, London, UK.
Background: Systemic sclerosis (SSc) is a rare connective tissue disease associated with rapidly evolving interstitial lung disease (ILD), driving its mortality. Specific imaging-based biomarkers associated with the evolution of lung disease are needed to help predict and quantify ILD.
Methods: We evaluated the potential of an automated ILD quantification system (icolung) from chest CT scans, to help in quantification and prediction of ILD progression in SSc-ILD.
Clinical benefits and risks of remote patient monitoring: an overview and assessment of methodological rigour of systematic reviews for selected patient groups.
BMC Health Serv Res
January 2025
Department of Planning and Development, Region Västra Götaland, NU Hospital Group, Trollhättan, Sweden.
Background: Remote patient monitoring implies continuous follow-up of health-related parameters of patients outside healthcare facilities. Patients share health-related data with their healthcare unit and obtain feedback (which may be automatically generated if data are within a predefined range). The goals of remote patient monitoring are improvements for patients and reduced healthcare costs.
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